1. Navigating Working Sets Brad Myers 05-899D: Human Aspects of Software Development (HASD) Spring, 2011 1 Copyright © 2011 – Brad Myers

2. Carnegie Mellon University, School of Computer Science 10 “expert” Java programmers 5 tasks, do in any order 1 program to maintain Eclipse 2.0 IDE for each task completed for documentation, examples, etc. Study of Design Requirements for Maintenance-Oriented IDEs [Ko 2005] $10the internet 2

3. Carnegie Mellon University, School of Computer Science Reading task descriptions Editing code Switching environments Reading the Java API Testing Paint Searching for task-related words Navigating dependencies Reading code 20% 22% 3% 5% 6% 13% 16% Time Spent on Different Activities 3

4. Carnegie Mellon University, School of Computer Science A Programmer’s Task task started task complete Find Read within file between files Edit Test Navigate... Form working set of task-relevant code Navigate dependencies in working set Modify code in working set 4

5. Carnegie Mellon University, School of Computer Science A Programmer’s Working Set A collection of task-relevant code fragments In modern software development, dependencies are distributed and non-local 5

6. Carnegie Mellon University, School of Computer Science Forming Working Sets How does ____ work? Searched for seemingly task- relevant words Only 50% of searches led to relevant code Why did(n’t) ____ happen? Formed hypotheses about potential causes of unexpected behavior 88% of hypotheses were false Programmers had trouble relating the behavior they saw (or didn’t see) to the code responsible for it. 6

7. Carnegie Mellon University, School of Computer Science Representing Working Sets Represented by explorer and file tabs When changing tasks, working sets were lost as tabs and nodes changed “Including” code in the working set by opening a file or expanding a node made it more difficult to navigate to other code in the working set 7

8. Carnegie Mellon University, School of Computer Science Navigating in a Working Set Navigated ~65 dependencies over 70 minutes 58% direct: Declaration of… Use of… Called by… Definition of… Some direct reference navigation supported by Eclipse commands 42% indirect: The method that computes the value that is passed to this method and used in this expression… Supported only by scroll bars, package explorer, tabs, and find 8

9. Carnegie Mellon University, School of Computer Science Times for Bottlenecks Each instance of an interactive bottleneck cost only a few seconds, but... = 35% of uninterrupted work time! Interactive BottleneckOverall Cost Navigating to fragment in same file (via scrolling) ~ 11 minutes Navigating to fragment in different file (via tabs and explorer) ~ 7 minutes Recovering working set after returning to a task ~ 1 minute Total Costs ~19 minutes 9

10. Carnegie Mellon University, School of Computer Science DeLine’s study of 7 developers [SoftViz 2005] Unfamiliar code, 4 tasks in lab for game of Tetris 2 subjects did top-down reasoning, 5 did bottom-up searching Confirmed Ko’s observation that: Navigating and “re-finding” areas of the code that had already been visited was frequent, difficult and distracting Textual searching and returning Tabs got problematic when many opened All subjects wanted better inline comments and overview documentation. Wanted code annotations All subjects agreed that finding the entry point and understanding the control flow was the most difficult task 10

11. Carnegie Mellon University, School of Computer Science de Alwis Study of Eclipse [2006] 8 IBM developers doing their own tasks using Eclipse for Java Interviews and 2-hour observations of actual use Experts do become disoriented Did use Eclipse’s advanced navigation tools, like find-all-callers No trace of how got to the current file, or how to get back Thrashing to view necessary context No support for switching tasks (digressions) 11

12. Carnegie Mellon University, School of Computer Science Multi-Dimensional Separation of Concerns [Tarr 1999] Software comprehensibility tends to degrade over time “Concern” – anything people want to consider as a conceptual unit Features, non-functional requirements, design idioms, and implementation mechanisms (e.g., caching) New features, such as saving to a file, can affect every class Invasive – into each class and interface Scattering – a single requirement affects multiple design and code modules Tangling – material pertaining to multiple requirements is interleaved within a single module Even though seem simple and orthogonal to express Subclassing and design patterns only work if is a pre-planned change “Tyranny of the dominant decomposition.” Introduces idea of “hyperslices” – decompose system separately along different dimensions May include same objects Doesn’t say how to implement them 12

13. Carnegie Mellon University, School of Computer Science Concern Graphs [Robillard 2007] Abstract (formal) model that describe which parts of the source code are relevant to different concerns FEAT tool builds concern graphs “semi-automatically” Shows only code relevant to the selected concern User-specified or detected using intra-concern analysis User can make queries Eval. using auth; 3 users; 4 users, auth 13

14. Carnegie Mellon University, School of Computer Science NavTracks [Singer 2005] Keeps track of navigations of developers Shows other files often navigated to from the current code file Evaluated on corpus of navigations 29% of the time, the file was in the list Case study of experiences of 5 users – 4 found it helpful Discusses why 14

15. Carnegie Mellon University, School of Computer Science Mylar – Kersten [2006] Over 90% of the changes committed to the Eclipse and Mozilla source repositories over a period of one year involved changes to more than one file 25% were significantly non-local changes Developers constantly switching tasks Mylar has explicit representation of the information related to a task Degree-of-interest (DOI) weighting for each program element User identifies tasks, Mylar keeps track of what relevant Files viewed, edited, saved Propagation (e.g., refactoring), prediction for indirect relationships Prediction based on searches using elements with high DOI 15

16. Carnegie Mellon University, School of Computer Science Mylar views 1 – task list 3 – package explorer filters to show what relevant to this task Most relevant are bold 4 – active search shows what might be relevant 5 – switch to different task 16

17. Carnegie Mellon University, School of Computer Science Mylar Evaluation Evaluation: longitudinal field study 99 (!) people used tool for 4 months (!) 1 week of training (control), then 1 week of active use Only 16 users resulted in sufficient data Edit ratio: edits/selections Mostly, quite positive change in edit ratio (p < 0.005) 84.17% of the selections events were of elements in the model with a positive DOI Observations: Mylar in active use by 1000s of people Tasks are not independent – need to share info. across tasks Different parts of lifecycle of tasks have different patterns 17

18. Carnegie Mellon University, School of Computer Science Jasper: Working Set Tool CSD MS thesis of Michael Coblenz [2006] Jasper = Java Aid with Sets of Pertinent Elements for Recall Allow programmers to grab arbitrary fragments of code to represent working sets Also notes, documentation, and other meta-information about code Allow programmers to view in one place, one screen Use for navigation, reminders, bookmarks Multiple working sets for different tasks 18

19. Carnegie Mellon University, School of Computer Science Implementation Eclipse plug-in 19

20. Carnegie Mellon University, School of Computer Science Code Set tool [Fraser 2008] Based on same observations as others Fields or methods that reference or are referenced from the current position Results of searches, breakpoints, etc. form searches Combine sets Informal lab study of 6 users 20

21. Carnegie Mellon University, School of Computer Science TeamTracks [DeLine 2005] Shows source code navigation patterns of other people on your team Related Items – most frequently visited either just before or after the selected item Favorite Classes – hide less frequently used Informal user study of 9 developers on Tetris game Deployed for real use – 5 developers for 3 weeks Successful, but usability issues, seemed most useful for newcomers 21

22. Carnegie Mellon University, School of Computer Science 22

23. Carnegie Mellon University, School of Computer Science TagSEA [Storey 2009] Tags for Software Engineering Activities Allow people to tag (annotate) software Reminding – often use comments for this Refinding – can use bookmarks, but not used by 84% of developers Shared among developers Added as //@Tag comments into the code Hierarchical, easily refactored Longitudinal study of 6 users’ real use over 8 weeks ½ adopted & used it actively Main use: Informational in support of future tasks navigation Artifact study of other kinds of tags in large codebases 23

24. Carnegie Mellon University, School of Computer Science TagSEA 24

25. Carnegie Mellon University, School of Computer Science ROSE [Zimmermann, 2005] Use version histories to determine show what usually edited at the same time 25

26. Carnegie Mellon University, School of Computer Science Code Bubbles [Bragdon 2010] Lightweight editable fragments Automatic layout & grouping (for working sets) Reflow of code so efficient space usage Use different areas for different tasks Eval: more code visible w/ fewer UI operations Lab study: 23 prof. dev. for creating, reading, debugging Think alouds & discussion 20 students doing debugging tasks Built on top of Eclipse 26

27. Carnegie Mellon University, School of Computer Science Code Bubbles Video 27